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Incidence is usually more useful than prevalence in understanding the disease etiology: for example, if the incidence rate of a disease in a population increases, then there is a risk factor that promotes the incidence. For example, consider a disease that takes a long time to cure and was widespread in 2002 but dissipated in 2003.
In epidemiology, a rate ratio, sometimes called an incidence density ratio or incidence rate ratio, is a relative difference measure used to compare the incidence rates of events occurring at any given point in time. It is defined as:
The incidence rate of a condition is the rate at which new cases occurred per person-year, for example, "2 new cases per 1,000 person-years". The cumulative incidence is the proportion of a population that became new cases within a specified time period, for example, "1.5 per 1,000 people became new cases during 2006".
Multi-country ecological studies of cancer incidence and mortality rates with respect to national diets have shown that some dietary factors such as animal products (meat, milk, fish and eggs), added sweeteners/sugar, and some fats appear to be risk factors for many types of cancer, while cereals/grains and vegetable products as a whole appear ...
For example, if the control group, using no treatment at all, had their own base rate of 1/20 recoveries within 1 day and a treatment had a 1/100 base rate of recovery within 1 day, we see that the treatment actively decreases the recovery. The base rate is an important concept in statistical inference, particularly in Bayesian statistics. [2]
Social statistics is the use of statistical measurement systems to study human behavior in a social environment. This can be accomplished through polling a group of people, evaluating a subset of data obtained about a group of people, or by observation and statistical analysis of a set of data that relates to people and their behaviors.
In statistics, a multimodal distribution is a probability distribution with more than one mode (i.e., more than one local peak of the distribution). These appear as distinct peaks (local maxima) in the probability density function , as shown in Figures 1 and 2.
A medical example is the likelihood that a given test result would be expected in a patient with a certain disorder compared to the likelihood that same result would occur in a patient without the target disorder. Some sources distinguish between LR+ and LR−. [13] A worked example is shown below.